import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv("C:/Users/akbar/Documents/R AED/iris.csv")
df

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

150 rows × 5 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Sepal.Length  150 non-null    float64
 1   Sepal.Width   150 non-null    float64
 2   Petal.Length  150 non-null    float64
 3   Petal.Width   150 non-null    float64
 4   Species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB

df.describe()

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

df.isnull().sum()

Sepal.Length    0
Sepal.Width     0
Petal.Length    0
Petal.Width     0
Species         0
dtype: int64

df.duplicated().sum()

1

df.shape

(150, 5)

df[df.duplicated()]

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
142	5.8	2.7	5.1	1.9	virginica

df['Species'].value_counts()

setosa        50
versicolor    50
virginica     50
Name: Species, dtype: int64

Exploratory Data Analysis

plt.figure(figsize=(8,6))
plt.title('Species Count')
sns.countplot(x="Species", data=df)

<AxesSubplot:title={'center':'Species Count'}, xlabel='Species', ylabel='count'>

plt.figure(figsize=(8,6))
sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x1f739f497f0>

<Figure size 576x432 with 0 Axes>

sns.heatmap(df.corr())

<AxesSubplot:>

df.columns.unique()

Index(['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width',
       'Species'],
      dtype='object')

for column_name in df.columns.unique():
    if column_name != "Species" :
        sns.displot(df[column_name], kde=True)

colors = ['BrBG_r', 'BuGn_r', 'BuPu_r', 'GnBu_r']
i = 0
f,axes=plt.subplots(4, figsize = (15,15))
for col,color in zip(df.columns.unique(), colors):
    if col != "Species" :
        sns.boxplot(x=col,data=df,ax=axes[i],palette=color)
        i = i+1

mp_pal = {"setosa" : "y", "versicolor" : "g", "virginica" : "r"}
i = 0
f,axes=plt.subplots(1,4, figsize = (20,6))
for col in df.columns.unique():
    if col != "Species" :
        sns.boxplot(x="Species",y = col,data=df,ax=axes[i],palette=mp_pal)
        i = i+1

setosa_df = df[df['Species']=="setosa"]
setosa_df['Petal.Length'].quantile(0.97)

1.806

setosa_df['Petal.Length'].quantile(0.03)

1.147

df[(df['Species']=="setosa")&(df['Petal.Length']>setosa_df['Petal.Length'].quantile(0.97))|(df['Petal.Length']<setosa_df['Petal.Length'].quantile(0.03))]

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
13	4.3	3.0	1.1	0.1	setosa
22	4.6	3.6	1.0	0.2	setosa
24	4.8	3.4	1.9	0.2	setosa
44	5.1	3.8	1.9	0.4	setosa

df.drop(index=[13,22,24,44]).head(15)

	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
5	5.4	3.9	1.7	0.4	setosa
6	4.6	3.4	1.4	0.3	setosa
7	5.0	3.4	1.5	0.2	setosa
8	4.4	2.9	1.4	0.2	setosa
9	4.9	3.1	1.5	0.1	setosa
10	5.4	3.7	1.5	0.2	setosa
11	4.8	3.4	1.6	0.2	setosa
12	4.8	3.0	1.4	0.1	setosa
14	5.8	4.0	1.2	0.2	setosa
15	5.7	4.4	1.5	0.4	setosa

Alternate EDA Method

!pip3 install pandas-profiling

Requirement already satisfied: pandas-profiling in c:\users\akbar\anaconda3\lib\site-packages (3.2.0)

WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC02C7C0>: Failed to establish a new connection: [WinError 10051] A socket operation was attempted to an unreachable network')': /simple/markupsafe/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC02CA60>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC02CBE0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC02CE20>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC05AEE0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC052040>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pandas-profiling/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC015670>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pandas-profiling/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC04B070>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pandas-profiling/
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC04B7C0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pandas-profiling/
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x00000215AC05A640>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pandas-profiling/
ERROR: Could not find a version that satisfies the requirement markupsafe~=2.1.1 (from pandas-profiling) (from versions: none)
ERROR: No matching distribution found for markupsafe~=2.1.1

Requirement already satisfied: multimethod>=1.4 in c:\users\akbar\anaconda3\lib\site-packages (from pandas-profiling) (1.8)
Requirement already satisfied: scipy>=1.4.1 in c:\users\akbar\anaconda3\lib\site-packages (from pandas-profiling) (1.7.1)
Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in c:\users\akbar\anaconda3\lib\site-packages (from pandas-profiling) (1.3.4)
Requirement already satisfied: missingno>=0.4.2 in c:\users\akbar\anaconda3\lib\site-packages (from pandas-profiling) (0.5.1)
Requirement already satisfied: requests>=2.24.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from pandas-profiling) (2.25.1)
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Requirement already satisfied: jinja2>=2.11.1 in c:\users\akbar\anaconda3\lib\site-packages (from pandas-profiling) (2.11.3)

!pip3 install MarkupSafe==2.1.1

WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023FDAE15850>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023FDAE1E1F0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023FDAE1E400>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023FDAE1E5B0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000023FDAE1E760>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/markupsafe/
ERROR: Could not find a version that satisfies the requirement MarkupSafe==2.1.1 (from versions: none)
ERROR: No matching distribution found for MarkupSafe==2.1.1

pip install --user --upgrade aws-sam-cli

Requirement already satisfied: aws-sam-cli in c:\users\akbar\appdata\roaming\python\python39\site-packages (1.53.0)Note: you may need to restart the kernel to use updated packages.

WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002065D51C2E0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/aws-sam-cli/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002065D51C4F0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/aws-sam-cli/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002065D51C6A0>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/aws-sam-cli/
WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002065D51C850>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/aws-sam-cli/
WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x000002065D51CA00>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/aws-sam-cli/

Requirement already satisfied: click~=7.1 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (7.1.2)
Requirement already satisfied: dateparser~=1.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (1.1.1)
Requirement already satisfied: regex==2021.9.30 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (2021.9.30)
Requirement already satisfied: tzlocal==3.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (3.0)
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Requirement already satisfied: chevron~=0.12 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (0.14.0)
Requirement already satisfied: watchdog==2.1.2 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (2.1.2)
Requirement already satisfied: serverlessrepo==0.1.10 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (0.1.10)
Requirement already satisfied: Flask~=1.1.4 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (1.1.4)
Requirement already satisfied: MarkupSafe==2.0.1 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (2.0.1)
Requirement already satisfied: jmespath~=0.10.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (0.10.0)
Requirement already satisfied: cookiecutter~=2.1.1 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (2.1.1)
Requirement already satisfied: requests==2.25.1 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (2.25.1)
Requirement already satisfied: aws-sam-translator==1.46.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (1.46.0)
Requirement already satisfied: PyYAML~=5.3 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (5.4.1)
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Requirement already satisfied: docker~=4.2.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (4.2.2)
Requirement already satisfied: tomlkit==0.7.2 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (0.7.2)
Requirement already satisfied: aws-lambda-builders==1.18.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from aws-sam-cli) (1.18.0)
Requirement already satisfied: setuptools in c:\users\akbar\anaconda3\lib\site-packages (from aws-lambda-builders==1.18.0->aws-sam-cli) (58.0.4)
Requirement already satisfied: wheel in c:\users\akbar\anaconda3\lib\site-packages (from aws-lambda-builders==1.18.0->aws-sam-cli) (0.37.0)
Requirement already satisfied: jsonschema~=3.2 in c:\users\akbar\anaconda3\lib\site-packages (from aws-sam-translator==1.46.0->aws-sam-cli) (3.2.0)
Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from boto3==1.*,>=1.19.5->aws-sam-cli) (0.6.0)
Requirement already satisfied: botocore<1.28.0,>=1.27.33 in c:\users\akbar\appdata\roaming\python\python39\site-packages (from boto3==1.*,>=1.19.5->aws-sam-cli) (1.27.33)
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Requirement already satisfied: tzdata in c:\users\akbar\appdata\roaming\python\python39\site-packages (from tzlocal==3.0->aws-sam-cli) (2022.1)
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from pandas_profiling import ProfileReport

dff = pd.read_csv('C:/Users/akbar/Documents/Coding Course/StackUp EDA Python/train.csv')

dff.head(5)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

prof = ProfileReport(dff)
prof.to_notebook_iframe()

dff.dtypes

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object

dff.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

dff.shape

(891, 12)

dff.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

dff['Survived'].value_counts(normalize = True)

0    0.616162
1    0.383838
Name: Survived, dtype: float64

sns.pairplot(dff,hue='Survived')

<seaborn.axisgrid.PairGrid at 0x1f73d89f8b0>

dff.isna().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

plt.figure(figsize=(12,8))
sns.heatmap(dff.isna().transpose(),
            cmap="YlGnBu",
           cbar_kws={'label':'Missing data'})

<AxesSubplot:>

dff['Ticket'].unique()

array(['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450',
       '330877', '17463', '349909', '347742', '237736', 'PP 9549',
       '113783', 'A/5. 2151', '347082', '350406', '248706', '382652',
       '244373', '345763', '2649', '239865', '248698', '330923', '113788',
       '347077', '2631', '19950', '330959', '349216', 'PC 17601',
       'PC 17569', '335677', 'C.A. 24579', 'PC 17604', '113789', '2677',
       'A./5. 2152', '345764', '2651', '7546', '11668', '349253',
       'SC/Paris 2123', '330958', 'S.C./A.4. 23567', '370371', '14311',
       '2662', '349237', '3101295', 'A/4. 39886', 'PC 17572', '2926',
       '113509', '19947', 'C.A. 31026', '2697', 'C.A. 34651', 'CA 2144',
       '2669', '113572', '36973', '347088', 'PC 17605', '2661',
       'C.A. 29395', 'S.P. 3464', '3101281', '315151', 'C.A. 33111',
       'S.O.C. 14879', '2680', '1601', '348123', '349208', '374746',
       '248738', '364516', '345767', '345779', '330932', '113059',
       'SO/C 14885', '3101278', 'W./C. 6608', 'SOTON/OQ 392086', '343275',
       '343276', '347466', 'W.E.P. 5734', 'C.A. 2315', '364500', '374910',
       'PC 17754', 'PC 17759', '231919', '244367', '349245', '349215',
       '35281', '7540', '3101276', '349207', '343120', '312991', '349249',
       '371110', '110465', '2665', '324669', '4136', '2627',
       'STON/O 2. 3101294', '370369', 'PC 17558', 'A4. 54510', '27267',
       '370372', 'C 17369', '2668', '347061', '349241',
       'SOTON/O.Q. 3101307', 'A/5. 3337', '228414', 'C.A. 29178',
       'SC/PARIS 2133', '11752', '7534', 'PC 17593', '2678', '347081',
       'STON/O2. 3101279', '365222', '231945', 'C.A. 33112', '350043',
       '230080', '244310', 'S.O.P. 1166', '113776', 'A.5. 11206',
       'A/5. 851', 'Fa 265302', 'PC 17597', '35851', 'SOTON/OQ 392090',
       '315037', 'CA. 2343', '371362', 'C.A. 33595', '347068', '315093',
       '363291', '113505', 'PC 17318', '111240', 'STON/O 2. 3101280',
       '17764', '350404', '4133', 'PC 17595', '250653', 'LINE',
       'SC/PARIS 2131', '230136', '315153', '113767', '370365', '111428',
       '364849', '349247', '234604', '28424', '350046', 'PC 17610',
       '368703', '4579', '370370', '248747', '345770', '3101264', '2628',
       'A/5 3540', '347054', '2699', '367231', '112277',
       'SOTON/O.Q. 3101311', 'F.C.C. 13528', 'A/5 21174', '250646',
       '367229', '35273', 'STON/O2. 3101283', '243847', '11813',
       'W/C 14208', 'SOTON/OQ 392089', '220367', '21440', '349234',
       '19943', 'PP 4348', 'SW/PP 751', 'A/5 21173', '236171', '347067',
       '237442', 'C.A. 29566', 'W./C. 6609', '26707', 'C.A. 31921',
       '28665', 'SCO/W 1585', '367230', 'W./C. 14263',
       'STON/O 2. 3101275', '2694', '19928', '347071', '250649', '11751',
       '244252', '362316', '113514', 'A/5. 3336', '370129', '2650',
       'PC 17585', '110152', 'PC 17755', '230433', '384461', '110413',
       '112059', '382649', 'C.A. 17248', '347083', 'PC 17582', 'PC 17760',
       '113798', '250644', 'PC 17596', '370375', '13502', '347073',
       '239853', 'C.A. 2673', '336439', '347464', '345778', 'A/5. 10482',
       '113056', '349239', '345774', '349206', '237798', '370373',
       '19877', '11967', 'SC/Paris 2163', '349236', '349233', 'PC 17612',
       '2693', '113781', '19988', '9234', '367226', '226593', 'A/5 2466',
       '17421', 'PC 17758', 'P/PP 3381', 'PC 17485', '11767', 'PC 17608',
       '250651', '349243', 'F.C.C. 13529', '347470', '29011', '36928',
       '16966', 'A/5 21172', '349219', '234818', '345364', '28551',
       '111361', '113043', 'PC 17611', '349225', '7598', '113784',
       '248740', '244361', '229236', '248733', '31418', '386525',
       'C.A. 37671', '315088', '7267', '113510', '2695', '2647', '345783',
       '237671', '330931', '330980', 'SC/PARIS 2167', '2691',
       'SOTON/O.Q. 3101310', 'C 7076', '110813', '2626', '14313',
       'PC 17477', '11765', '3101267', '323951', 'C 7077', '113503',
       '2648', '347069', 'PC 17757', '2653', 'STON/O 2. 3101293',
       '349227', '27849', '367655', 'SC 1748', '113760', '350034',
       '3101277', '350052', '350407', '28403', '244278', '240929',
       'STON/O 2. 3101289', '341826', '4137', '315096', '28664', '347064',
       '29106', '312992', '349222', '394140', 'STON/O 2. 3101269',
       '343095', '28220', '250652', '28228', '345773', '349254',
       'A/5. 13032', '315082', '347080', 'A/4. 34244', '2003', '250655',
       '364851', 'SOTON/O.Q. 392078', '110564', '376564', 'SC/AH 3085',
       'STON/O 2. 3101274', '13507', 'C.A. 18723', '345769', '347076',
       '230434', '65306', '33638', '113794', '2666', '113786', '65303',
       '113051', '17453', 'A/5 2817', '349240', '13509', '17464',
       'F.C.C. 13531', '371060', '19952', '364506', '111320', '234360',
       'A/S 2816', 'SOTON/O.Q. 3101306', '113792', '36209', '323592',
       '315089', 'SC/AH Basle 541', '7553', '31027', '3460', '350060',
       '3101298', '239854', 'A/5 3594', '4134', '11771', 'A.5. 18509',
       '65304', 'SOTON/OQ 3101317', '113787', 'PC 17609', 'A/4 45380',
       '36947', 'C.A. 6212', '350035', '315086', '364846', '330909',
       '4135', '26360', '111427', 'C 4001', '382651', 'SOTON/OQ 3101316',
       'PC 17473', 'PC 17603', '349209', '36967', 'C.A. 34260', '226875',
       '349242', '12749', '349252', '2624', '2700', '367232',
       'W./C. 14258', 'PC 17483', '3101296', '29104', '2641', '2690',
       '315084', '113050', 'PC 17761', '364498', '13568', 'WE/P 5735',
       '2908', '693', 'SC/PARIS 2146', '244358', '330979', '2620',
       '347085', '113807', '11755', '345572', '372622', '349251',
       '218629', 'SOTON/OQ 392082', 'SOTON/O.Q. 392087', 'A/4 48871',
       '349205', '2686', '350417', 'S.W./PP 752', '11769', 'PC 17474',
       '14312', 'A/4. 20589', '358585', '243880', '2689',
       'STON/O 2. 3101286', '237789', '13049', '3411', '237565', '13567',
       '14973', 'A./5. 3235', 'STON/O 2. 3101273', 'A/5 3902', '364848',
       'SC/AH 29037', '248727', '2664', '349214', '113796', '364511',
       '111426', '349910', '349246', '113804', 'SOTON/O.Q. 3101305',
       '370377', '364512', '220845', '31028', '2659', '11753', '350029',
       '54636', '36963', '219533', '349224', '334912', '27042', '347743',
       '13214', '112052', '237668', 'STON/O 2. 3101292', '350050',
       '349231', '13213', 'S.O./P.P. 751', 'CA. 2314', '349221', '8475',
       '330919', '365226', '349223', '29751', '2623', '5727', '349210',
       'STON/O 2. 3101285', '234686', '312993', 'A/5 3536', '19996',
       '29750', 'F.C. 12750', 'C.A. 24580', '244270', '239856', '349912',
       '342826', '4138', '330935', '6563', '349228', '350036', '24160',
       '17474', '349256', '2672', '113800', '248731', '363592', '35852',
       '348121', 'PC 17475', '36864', '350025', '223596', 'PC 17476',
       'PC 17482', '113028', '7545', '250647', '348124', '34218', '36568',
       '347062', '350048', '12233', '250643', '113806', '315094', '36866',
       '236853', 'STON/O2. 3101271', '239855', '28425', '233639',
       '349201', '349218', '16988', '376566', 'STON/O 2. 3101288',
       '250648', '113773', '335097', '29103', '392096', '345780',
       '349204', '350042', '29108', '363294', 'SOTON/O2 3101272', '2663',
       '347074', '112379', '364850', '8471', '345781', '350047',
       'S.O./P.P. 3', '2674', '29105', '347078', '383121', '36865',
       '2687', '113501', 'W./C. 6607', 'SOTON/O.Q. 3101312', '374887',
       '3101265', '12460', 'PC 17600', '349203', '28213', '17465',
       '349244', '2685', '2625', '347089', '347063', '112050', '347087',
       '248723', '3474', '28206', '364499', '112058', 'STON/O2. 3101290',
       'S.C./PARIS 2079', 'C 7075', '315098', '19972', '368323', '367228',
       '2671', '347468', '2223', 'PC 17756', '315097', '392092', '11774',
       'SOTON/O2 3101287', '2683', '315090', 'C.A. 5547', '349213',
       '347060', 'PC 17592', '392091', '113055', '2629', '350026',
       '28134', '17466', '233866', '236852', 'SC/PARIS 2149', 'PC 17590',
       '345777', '349248', '695', '345765', '2667', '349212', '349217',
       '349257', '7552', 'C.A./SOTON 34068', 'SOTON/OQ 392076', '211536',
       '112053', '111369', '370376'], dtype=object)

dff['Sex'].unique()

array(['male', 'female'], dtype=object)

dff.drop(columns=['PassengerId','Cabin','Ticket'], inplace=True)

plt.figure(figsize=(12,8))
sns.set_style("whitegrid")
sns.countplot(x='Survived', data=dff)

<AxesSubplot:xlabel='Survived', ylabel='count'>

plt.figure(figsize=(12,8))
sns.set_style("whitegrid")
sns.countplot(x='Survived',hue='Sex',data=dff)

<AxesSubplot:xlabel='Survived', ylabel='count'>

plt.figure(figsize=(12,8))
sns.set_style("whitegrid")
sns.countplot(x='Survived',hue='Pclass',data=dff)

<AxesSubplot:xlabel='Survived', ylabel='count'>

sns.displot(dff['Age'].dropna(),kde=False,bins=20,color='darkgreen')

<seaborn.axisgrid.FacetGrid at 0x1f73fb06070>

plt.figure(figsize=(12,8))
sns.set_style("whitegrid")
sns.countplot(x='Survived',hue='SibSp',data=dff)

<AxesSubplot:xlabel='Survived', ylabel='count'>

dff['Fare'].hist(color='darkgreen',
                bins=30,
                figsize=(12,8))

<AxesSubplot:>

plt.figure(figsize=(12,8))
sns.histplot(x='Fare',hue='Survived',data=dff,bins=20)

<AxesSubplot:xlabel='Fare', ylabel='Count'>

plt.figure(figsize=(16,12))
sns.boxplot(x='Pclass',y='Age',
            hue=dff[['Sex','Survived']].apply(tuple, axis=1),
            data=dff,palette='PuOr_r')

<AxesSubplot:xlabel='Pclass', ylabel='Age'>

def impute_age(cols):
    Age = cols[0]
    Pclass = cols[1]
    Survived = cols[2]
    gender = cols[3]
    
    if pd.isnull(Age):
        if Pclass == 1 and Survived == 0 and gender == 'male':
            return 46
        elif Pclass == 1 and Survived == 1 and gender == 'male':
            return 35
        elif Pclass == 1 and Survived == 0 and gender == 'female':
            return 25
        elif Pclass == 1 and Survived == 1 and gender == 'female':
            return 35
        elif Pclass == 2 and Survived == 0 and gender == 'male':
            return 31
        elif Pclass == 2 and Survived == 1 and gender == 'male':
            return 3
        elif Pclass == 2 and Survived == 0 and gender == 'female':
            return 33
        elif Pclass == 2 and Survived == 1 and gender == 'female':
            return 28
        elif Pclass == 3 and Survived == 0 and gender == 'male':
            return 25
        elif Pclass == 3 and Survived == 1 and gender == 'male':
            return 25
        elif Pclass == 3 and Survived == 0 and gender == 'female':
            return 22
        elif Pclass == 3 and Survived == 1 and gender == 'female':
            return 19
        else:
            return(Age)
    else:
        return(Age)

dff['Age'] = dff[['Age','Pclass','Survived','Sex']].apply(impute_age,axis=1)

dff.head(15)

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Fare	Embarked
0	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	7.2500	S
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	71.2833	C
2	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	7.9250	S
3	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	53.1000	S
4	0	3	Allen, Mr. William Henry	male	35.0	0	0	8.0500	S
5	0	3	Moran, Mr. James	male	25.0	0	0	8.4583	Q
6	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	51.8625	S
7	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	21.0750	S
8	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	11.1333	S
9	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	30.0708	C
10	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	16.7000	S
11	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	26.5500	S
12	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	8.0500	S
13	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	31.2750	S
14	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	7.8542	S

dff.isnull().sum()

Survived    0
Pclass      0
Name        0
Sex         0
Age         0
SibSp       0
Parch       0
Fare        0
Embarked    2
dtype: int64

def name_title(name):
    
    if('Mr') in name:
        return('Mr')
    elif('Mrs') in name:
        return('Mrs')
    elif('Miss') in name:
        return('Miss')
    elif('Rev') in name:
        return('Rev')
    else:
        return(np.nan)

dff['title'] = dff['Name'].apply(name_title)

dff[dff['title'].isnull()]

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Fare	Embarked	title
7	0	3	Palsson, Master. Gosta Leonard	male	2.00	3	1	21.0750	S	NaN
16	0	3	Rice, Master. Eugene	male	2.00	4	1	29.1250	Q	NaN
30	0	1	Uruchurtu, Don. Manuel E	male	40.00	0	0	27.7208	C	NaN
50	0	3	Panula, Master. Juha Niilo	male	7.00	4	1	39.6875	S	NaN
59	0	3	Goodwin, Master. William Frederick	male	11.00	5	2	46.9000	S	NaN
63	0	3	Skoog, Master. Harald	male	4.00	3	2	27.9000	S	NaN
65	1	3	Moubarek, Master. Gerios	male	25.00	1	1	15.2458	C	NaN
78	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	29.0000	S	NaN
125	1	3	Nicola-Yarred, Master. Elias	male	12.00	1	0	11.2417	C	NaN
159	0	3	Sage, Master. Thomas Henry	male	25.00	8	2	69.5500	S	NaN
164	0	3	Panula, Master. Eino Viljami	male	1.00	4	1	39.6875	S	NaN
165	1	3	Goldsmith, Master. Frank John William "Frankie"	male	9.00	0	2	20.5250	S	NaN
171	0	3	Rice, Master. Arthur	male	4.00	4	1	29.1250	Q	NaN
176	0	3	Lefebre, Master. Henry Forbes	male	25.00	3	1	25.4667	S	NaN
182	0	3	Asplund, Master. Clarence Gustaf Hugo	male	9.00	4	2	31.3875	S	NaN
183	1	2	Becker, Master. Richard F	male	1.00	2	1	39.0000	S	NaN
193	1	2	Navratil, Master. Michel M	male	3.00	1	1	26.0000	S	NaN
245	0	1	Minahan, Dr. William Edward	male	44.00	2	0	90.0000	Q	NaN
261	1	3	Asplund, Master. Edvin Rojj Felix	male	3.00	4	2	31.3875	S	NaN
278	0	3	Rice, Master. Eric	male	7.00	4	1	29.1250	Q	NaN
305	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	151.5500	S	NaN
317	0	2	Moraweck, Dr. Ernest	male	54.00	0	0	14.0000	S	NaN
340	1	2	Navratil, Master. Edmond Roger	male	2.00	1	1	26.0000	S	NaN
348	1	3	Coutts, Master. William Loch "William"	male	3.00	1	1	15.9000	S	NaN
369	1	1	Aubart, Mme. Leontine Pauline	female	24.00	0	0	69.3000	C	NaN
386	0	3	Goodwin, Master. Sidney Leonard	male	1.00	5	2	46.9000	S	NaN
398	0	2	Pain, Dr. Alfred	male	23.00	0	0	10.5000	S	NaN
407	1	2	Richards, Master. William Rowe	male	3.00	1	1	18.7500	S	NaN
443	1	2	Reynaldo, Ms. Encarnacion	female	28.00	0	0	13.0000	S	NaN
445	1	1	Dodge, Master. Washington	male	4.00	0	2	81.8583	S	NaN
449	1	1	Peuchen, Major. Arthur Godfrey	male	52.00	0	0	30.5000	S	NaN
480	0	3	Goodwin, Master. Harold Victor	male	9.00	5	2	46.9000	S	NaN
489	1	3	Coutts, Master. Eden Leslie "Neville"	male	9.00	1	1	15.9000	S	NaN
536	0	1	Butt, Major. Archibald Willingham	male	45.00	0	0	26.5500	S	NaN
549	1	2	Davies, Master. John Morgan Jr	male	8.00	1	1	36.7500	S	NaN
632	1	1	Stahelin-Maeglin, Dr. Max	male	32.00	0	0	30.5000	C	NaN
641	1	1	Sagesser, Mlle. Emma	female	24.00	0	0	69.3000	C	NaN
647	1	1	Simonius-Blumer, Col. Oberst Alfons	male	56.00	0	0	35.5000	C	NaN
660	1	1	Frauenthal, Dr. Henry William	male	50.00	2	0	133.6500	S	NaN
694	0	1	Weir, Col. John	male	60.00	0	0	26.5500	S	NaN
709	1	3	Moubarek, Master. Halim Gonios ("William George")	male	25.00	1	1	15.2458	C	NaN
745	0	1	Crosby, Capt. Edward Gifford	male	70.00	1	1	71.0000	S	NaN
751	1	3	Moor, Master. Meier	male	6.00	0	1	12.4750	S	NaN
755	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	14.5000	S	NaN
759	1	1	Rothes, the Countess. of (Lucy Noel Martha Dye...	female	33.00	0	0	86.5000	S	NaN
766	0	1	Brewe, Dr. Arthur Jackson	male	46.00	0	0	39.6000	C	NaN
787	0	3	Rice, Master. George Hugh	male	8.00	4	1	29.1250	Q	NaN
788	1	3	Dean, Master. Bertram Vere	male	1.00	1	2	20.5750	S	NaN
796	1	1	Leader, Dr. Alice (Farnham)	female	49.00	0	0	25.9292	S	NaN
802	1	1	Carter, Master. William Thornton II	male	11.00	1	2	120.0000	S	NaN
803	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	8.5167	C	NaN
819	0	3	Skoog, Master. Karl Thorsten	male	10.00	3	2	27.9000	S	NaN
822	0	1	Reuchlin, Jonkheer. John George	male	38.00	0	0	0.0000	S	NaN
824	0	3	Panula, Master. Urho Abraham	male	2.00	4	1	39.6875	S	NaN
827	1	2	Mallet, Master. Andre	male	1.00	0	2	37.0042	C	NaN
831	1	2	Richards, Master. George Sibley	male	0.83	1	1	18.7500	S	NaN
850	0	3	Andersson, Master. Sigvard Harald Elias	male	4.00	4	2	31.2750	S	NaN
869	1	3	Johnson, Master. Harold Theodor	male	4.00	1	1	11.1333	S	NaN

def name_title(name):
    
    if('Mr') in name:
        return('Mr')
    elif('Mrs') in name:
        return('Mrs')
    elif('Miss') in name:
        return('Miss')
    elif('Ms') in name:
        return('Miss')
    elif('Rev') in name:
        return('Rev')
    elif('Master') in name:
        return('Master')
    elif('Don') in name:
        return('Don')
    elif('Dr') in name:
        return('Dr')
    elif('Mme') in name:
        return('Mme')
    elif('Major') in name:
        return('Major')
    elif('Mlle') in name:
        return('Mlle')
    elif('Col') in name:
        return('Col')
    elif('Countess') in name:
        return('Countess')
    elif('Leader') in name:
        return('Leader')
    elif('Jonkheer') in name:
        return('Jonkheer')
    elif('Capt') in name:
        return('Capt')
    else:
        return(np.nan)

dff['title'] = dff['Name'].apply(name_title)
dff[dff['title'].isnull()]

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Fare	Embarked	title

dff.head(10)

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Fare	Embarked	title
0	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	7.2500	S	Mr
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	71.2833	C	Mr
2	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	7.9250	S	Miss
3	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	53.1000	S	Mr
4	0	3	Allen, Mr. William Henry	male	35.0	0	0	8.0500	S	Mr
5	0	3	Moran, Mr. James	male	25.0	0	0	8.4583	Q	Mr
6	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	51.8625	S	Mr
7	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	21.0750	S	Master
8	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	11.1333	S	Mr
9	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	30.0708	C	Mr

plt.figure(figsize=(12,8))
sns.set_style("whitegrid")
sns.countplot(x='title', data=dff)

<AxesSubplot:xlabel='title', ylabel='count'>

gender = pd.get_dummies(dff['Sex'],drop_first=True)
embark = pd.get_dummies(dff['Embarked'],drop_first=True)
title = pd.get_dummies(dff['title'])

dff.drop(['Sex','Embarked','Name','title'], axis=1, inplace=True)

ds = pd.concat([dff,gender,embark,title], axis=1)

ds.head(10)

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S	Capt	...	Don	Dr	Jonkheer	Major	Master	Miss	Mlle	Mme	Mr	Rev
0	0	3	22.0	1	0	7.2500	1	0	1	0	...	0	0	0	0	0	0	0	0	1	0
1	1	1	38.0	1	0	71.2833	0	0	0	0	...	0	0	0	0	0	0	0	0	1	0
2	1	3	26.0	0	0	7.9250	0	0	1	0	...	0	0	0	0	0	1	0	0	0	0
3	1	1	35.0	1	0	53.1000	0	0	1	0	...	0	0	0	0	0	0	0	0	1	0
4	0	3	35.0	0	0	8.0500	1	0	1	0	...	0	0	0	0	0	0	0	0	1	0
5	0	3	25.0	0	0	8.4583	1	1	0	0	...	0	0	0	0	0	0	0	0	1	0
6	0	1	54.0	0	0	51.8625	1	0	1	0	...	0	0	0	0	0	0	0	0	1	0
7	0	3	2.0	3	1	21.0750	1	0	1	0	...	0	0	0	0	1	0	0	0	0	0
8	1	3	27.0	0	2	11.1333	0	0	1	0	...	0	0	0	0	0	0	0	0	1	0
9	1	2	14.0	1	0	30.0708	0	0	0	0	...	0	0	0	0	0	0	0	0	1	0

10 rows × 22 columns

ds.drop(['Jonkheer'],axis=1,inplace=True)

plt.figure(figsize=(16,12))
sns.heatmap(ds.corr())

<AxesSubplot:>

ds.to_csv('C:/Users/akbar/Documents/Coding Course/StackUp EDA Python/cleaned.csv')